Addressing fairness issues in deep learning-based medical image analysis: a systematic review
Deep learning algorithms have demonstrated remarkable efficacy in various medical image
analysis (MedIA) applications. However, recent research highlights a performance disparity …
analysis (MedIA) applications. However, recent research highlights a performance disparity …
Fast diffusion-based counterfactuals for shortcut removal and generation
Shortcut learning is when a model–eg a cardiac disease classifier–exploits correlations
between the target label and a spurious shortcut feature, eg a pacemaker, to predict the …
between the target label and a spurious shortcut feature, eg a pacemaker, to predict the …
MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images
Distributed collaborative learning is a promising approach for building predictive models for
privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model …
privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model …
Shortcut learning in medical image segmentation
Shortcut learning is a phenomenon where machine learning models prioritize learning
simple, potentially misleading cues from data that do not generalize well beyond the training …
simple, potentially misleading cues from data that do not generalize well beyond the training …
Augmenting chest x-ray datasets with non-expert annotations
C Damgaard, TN Eriksen, D Juodelyte… - arXiv preprint arXiv …, 2023 - arxiv.org
The advancement of machine learning algorithms in medical image analysis requires the
expansion of training datasets. A popular and cost-effective approach is automated …
expansion of training datasets. A popular and cost-effective approach is automated …
Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control
Abstract Machine learning (ML) models often fail with data that deviates from their training
distribution. This is a significant concern for ML-enabled devices as data drift may lead to …
distribution. This is a significant concern for ML-enabled devices as data drift may lead to …
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations
Spurious correlation caused by subgroup underrepresentation has received increasing
attention as a source of bias that can be perpetuated by deep neural networks (DNNs) …
attention as a source of bias that can be perpetuated by deep neural networks (DNNs) …
Towards actionability for open medical imaging datasets: lessons from community-contributed platforms for data management and stewardship
Medical imaging datasets are fundamental to artificial intelligence (AI) in healthcare. The
accuracy, robustness and fairness of diagnostic algorithms depend on the data (and its …
accuracy, robustness and fairness of diagnostic algorithms depend on the data (and its …
Feature Selection-driven Bias Deduction in Histopathology Images: Tackling Site-Specific Influences
The emergence of bias in deep neural models represents a significant reliability concern,
which may lead to overoptimistic results on seen data while compromising the model's …
which may lead to overoptimistic results on seen data while compromising the model's …
Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis Using Slice Discovery Methods
Abstract Machine learning models have achieved high overall accuracy in medical image
analysis. However, performance disparities on specific patient groups pose challenges to …
analysis. However, performance disparities on specific patient groups pose challenges to …